library(ebm)
data("Hitters", package = "ISLR2")
# Remove rows with missing response values
head(hitters <- Hitters[!is.na(Hitters$Salary), ])
#> AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits CHmRun
#> -Alan Ashby 315 81 7 24 38 39 14 3449 835 69
#> -Alvin Davis 479 130 18 66 72 76 3 1624 457 63
#> -Andre Dawson 496 141 20 65 78 37 11 5628 1575 225
#> -Andres Galarraga 321 87 10 39 42 30 2 396 101 12
#> -Alfredo Griffin 594 169 4 74 51 35 11 4408 1133 19
#> -Al Newman 185 37 1 23 8 21 2 214 42 1
#> CRuns CRBI CWalks League Division PutOuts Assists Errors
#> -Alan Ashby 321 414 375 N W 632 43 10
#> -Alvin Davis 224 266 263 A W 880 82 14
#> -Andre Dawson 828 838 354 N E 200 11 3
#> -Andres Galarraga 48 46 33 N E 805 40 4
#> -Alfredo Griffin 501 336 194 A W 282 421 25
#> -Al Newman 30 9 24 N E 76 127 7
#> Salary NewLeague
#> -Alan Ashby 475.0 N
#> -Alvin Davis 480.0 A
#> -Andre Dawson 500.0 N
#> -Andres Galarraga 91.5 N
#> -Alfredo Griffin 750.0 A
#> -Al Newman 70.0 A
# Fit a default EBM regressor
fit <- ebm(Salary ~ ., data = hitters, objective = "rmse")
fit # still need to implement print() and summary() methods
#> ExplainableBoostingRegressor(early_stopping_tolerance=0)
head(predict(fit, newdata = hitters))
#> [1] 489.4548 626.1970 870.3430 169.8797 659.6543 270.5382
head(predict(fit, newdata = hitters, se.fit = TRUE))
#> [,1] [,2]
#> [1,] 489.4548 53.92471
#> [2,] 626.1970 98.39167
#> [3,] 870.3430 241.04199
#> [4,] 169.8797 43.14385
#> [5,] 659.6543 54.02381
#> [6,] 270.5382 155.71903
plot(fit, display = "markdown")
plot(fit, term = "Years", display = "markdown")
fit$monotonize("Years", increasing = FALSE)
#> ExplainableBoostingRegressor(early_stopping_tolerance=0)
plot(fit, term = "Years", display = "markdown")